Meta-analytic evidence for a core problem solving network across multiple representational domains.
Meta-analytic evidence for a core problem solving network across multiple representational domains.
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DOI：
10.1016/j.neubiorev.2018.06.009
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发表时间：
2018-09
影响因子：
8.2
通讯作者：
Laird AR
中科院分区：
文献类型：
--
作者：
Bartley JE;Boeving ER;Riedel MC;Bottenhorn KL;Salo T;Eickhoff SB;Brewe E;Sutherland MT;Laird AR
解决问题是一项复杂的技能，涉及多步推理过程，以找到未知的解决方案。需要解决问题的现实环境的广度反映在类似的广泛但不集中的神经影像学文献中，并且与解决问题相关的一般领域或特定环境的大脑网络没有得到很好的理解。为了更充分地表征这些大脑网络，我们对131篇论文中来自1919个人的3166个焦点的280个神经成像问题解决实验进行了激活似然估计荟萃分析。问题解决的总体地图显示了广泛的额-扣谷-顶叶收敛，当考虑单独的数学、语言和视觉空间问题解决领域特定分析时，这些区域也同样被识别出来。结合分析揭示了一个支持跨不同上下文解决问题的共同网络，差异图区分了特定于任务类型的功能选择子网络。我们的研究结果表明，表征专用子网络和全脑系统之间的合作为问题解决提供了神经基础，核心网络提供了通用资源来执行认知操作和管理问题需求。跨网络动态的进一步表征可以为解决问题技能发展的神经教育研究提供信息。
Problem solving is a complex skill engaging multi-stepped reasoning processes to find unknown solutions. The breadth of real-world contexts requiring problem solving is mirrored by a similarly broad, yet unfocused neuroimaging literature, and the domain-general or context-specific brain networks associated with problem solving are not well understood. To more fully characterize those brain networks, we performed activation likelihood estimation meta-analysis on 280 neuroimaging problem solving experiments reporting 3166 foci from 1919 individuals across 131 papers. The general map of problem solving revealed broad fronto-cingulo-parietal convergence, regions similarly identified when considering separate mathematical, verbal, and visuospatial problem solving domain-specific analyses. Conjunction analysis revealed a common network supporting problem solving across diverse contexts, and difference maps distinguished functionally-selective sub-networks specific to task type. Our results suggest cooperation between representationally specialized sub-network and whole-brain systems provide a neural basis for problem solving, with the core network contributing general purpose resources to perform cognitive operations and manage problem demand. Further characterization of cross-network dynamics could inform neuroeducational studies on problem solving skill development.
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影响因子：
5.7
作者：
Bzdok D;Heeger A;Langner R;Laird AR;Fox PT;Palomero-Gallagher N;Vogt BA;Zilles K;Eickhoff SB
通讯作者：
Eickhoff SB
影响因子：
3.7
作者：
Bunge, SA;Wendelken, C;Wagner, AD
通讯作者：
Wagner, AD
影响因子：
5.7
作者：
Arsalidou, Marie;Taylor, Margot J.
通讯作者：
Taylor, Margot J.
影响因子：
4.6
作者：
Blackwell, Lisa S.;Trzesniewski, Kali H.;Dweck, Carol Sorich
通讯作者：
Dweck, Carol Sorich
影响因子：
25
作者：
Bassett DS;Yang M;Wymbs NF;Grafton ST
通讯作者：
Grafton ST